The present invention relates to communication systems, and more particularly to multiple-input multiple-output wireless systems.
Wireless communication systems typically use band-limited channels with time-varying (unknown) distortion and may have multi-users (such as multiple clients in a wireless LAN). This leads to intersymbol interference and multi-user interference, and requires interference-resistant detection for systems which are interference limited. Interference-limited systems include multi-antenna systems with multi-stream or space-time coding which have spatial interference, multi-tone systems, TDMA systems having frequency selective channels with long impulse responses leading to intersymbol interference, CDMA systems with multi-user interference arising from loss of orthogonality of spreading codes, high data rate CDMA which in addition to multi-user interference also has intersymbol interference.
Interference-resistant detectors commonly invoke one of three types of equalization to combat the interference: maximum likelihood sequence estimation, (adaptive) linear filtering, and decision-feedback equalization. However, maximum likelihood sequence estimation has problems including impractically large computation complexity for systems with multiple transmit antennas and multiple receive antennas. Linear filtering equalization, such as linear zero-forcing and linear minimum square error equalization, has low computational complexity but has relatively poor performance due to excessive noise enhancement. The decision-feedback (iterative) detectors, such as iterative zero-forcing and iterative minimum mean square error, have moderate computational complexity and exhibits superior performance compared to linear receivers.
In an iterative receiver, the symbols from a transmit antenna are first detected. The contribution due to these symbols in the received signal is then removed, followed by detection of symbols from the second transmit antenna. This procedure is followed until all transmitted symbols are detected. Since the soft symbols and soft bits are estimated in the presence of noise, they have to be scaled appropriately before being fed into the Viterbi decoder for the recovery of transmitted bits.